Abstract-Feature Selection (FS) is an important process to find the minimal subset of features from the original data by removing the redundant and irrelevant features. It aims to improve the efficiency of classification algorithms. Rough set theory (RST) is one of the effective approaches to feature selection, but it uses complete search to search for all subsets of features and dependency to evaluate these subsets. However, the complete search is expensive and may not be feasible for large data due to its high cost. Therefore, meta-heuristics algorithms, especially Nature Inspired Algorithms, have been widely used to replace the reduction part in RST. This paper develops a new algorithm for Feature Selection based on hybrid Binary Cuckoo Search and rough set theory for classification on nominal datasets. The developed algorithm is evaluated on five nominal datasets from the UCI repository, against a number of similar NIAs algorithms. The results show that our algorithm achieves better FS compared to two known NIAs in a lesser number of iterations, without significantly reducing the classification accuracy.

Crowded event entrances could threaten the comfort and safety of pedestrians, especially when some pedestrians push others or use gaps in crowds to gain faster access to an event. Studying and understanding pushing dynamics leads to designing and building more comfortable and safe entrances. Researchers—to understand pushing dynamics—observe and analyze recorded videos to manually identify when and where pushing behavior occurs. Despite the accuracy of the manual method, it can still be time-consuming, tedious, and hard to identify pushing behavior in some scenarios. In this article, we propose a hybrid deep learning and visualization framework that aims to assist researchers in automatically identifying pushing behavior in videos. The proposed framework comprises two main components: (i) Deep optical flow and wheel visualization; to generate motion information maps. (ii) A combination of an EfficientNet-B0-based classifier and a false reduction algorithm for detecting pushing behavior at the video patch level. In addition to the framework, we present a new patch-based approach to enlarge the data and alleviate the class imbalance problem in small-scale pushing behavior datasets. Experimental results (using real-world ground truth of pushing behavior videos) demonstrate that the proposed framework achieves an 86% accuracy rate. Moreover, the EfficientNet-B0-based classifier outperforms baseline CNN-based classifiers in terms of accuracy.

Redundant and irrelevant features in datasets decrease classification accuracy, and increase computational time of classification algorithms, overfitting problem and complexity of the underlying classification model. Feature selection is a preprocessing technique used in classification algorithms to improve the selection of relevant features. Several approaches that combine Rough Set Theory (RST) with Nature Inspired Algorithms (NIAs) have been used successfully for feature selection. However, due to the inherit limitations of RST for some data types and the inefficient convergence of NIAs for high dimensional datasets, these approaches have mainly focused on a specific type of low dimensional nominal dataset. This paper proposes a new filter feature selection approach based on Binary Cuckoo Search (BCS) and RST, which is more efficient for low and high dimensional nominal, mixed and numerical datasets. It enhances BCS by developing a new initialization and global update mechanisms to increase the efficiency of convergence for high dimensional datasets. It also develops a more efficient objective function for numerical, mixed and nominal datasets. The proposed approach was validated on 16 benchmark datasets; 4 nominal, 4 mixed and 8 numerical drawn from the UCI repository. It was also evaluated against standard BCS; five NIAs with fuzzy RST approaches; two popular traditional FS approaches; and multi objective evolutionary, Genetic, and Particle Swarm Optimization (PSO) algorithms. Decision tree and naive Bayes algorithms were used to measure the classification performance of the proposed approach. The results show that the proposed approach achieved improved classification accuracy while minimizing the number of features compared to other state-of-the-art methods. The code is available at https://github.com/abualia4/EBCS.